Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations73450
Missing cells0
Missing cells (%)0.0%
Duplicate rows55
Duplicate rows (%)0.1%
Total size in memory21.9 MiB
Average record size in memory312.0 B

Variable types

Numeric7
Categorical31

Alerts

number_outpatient has constant value "0" Constant
number_emergency has constant value "0" Constant
examide_category has constant value "0" Constant
citoglipton_category has constant value "0" Constant
metformin-rosiglitazone_category has constant value "0" Constant
Dataset has 55 (0.1%) duplicate rowsDuplicates
age_category is highly overall correlated with age_meanHigh correlation
age_mean is highly overall correlated with age_categoryHigh correlation
change_category is highly overall correlated with diabetes-medication_category and 1 other fieldsHigh correlation
diabetes-medication_category is highly overall correlated with change_category and 1 other fieldsHigh correlation
insulin_category is highly overall correlated with change_category and 1 other fieldsHigh correlation
metformin_category is highly imbalanced (59.4%) Imbalance
repaglinide_category is highly imbalanced (94.2%) Imbalance
nateglinide_category is highly imbalanced (97.1%) Imbalance
chlorpropamide_category is highly imbalanced (99.4%) Imbalance
glimepiride_category is highly imbalanced (84.1%) Imbalance
acetohexamide_category is highly imbalanced (> 99.9%) Imbalance
glipizide_category is highly imbalanced (69.5%) Imbalance
glyburide_category is highly imbalanced (71.8%) Imbalance
tolbutamide_category is highly imbalanced (99.7%) Imbalance
pioglitazone_category is highly imbalanced (80.8%) Imbalance
rosiglitazone_category is highly imbalanced (82.5%) Imbalance
acarbose_category is highly imbalanced (98.3%) Imbalance
miglitol_category is highly imbalanced (99.7%) Imbalance
troglitazone_category is highly imbalanced (99.9%) Imbalance
tolazamide_category is highly imbalanced (99.6%) Imbalance
glyburide-metformin_category is highly imbalanced (97.0%) Imbalance
glipizide-metformin_category is highly imbalanced (99.9%) Imbalance
glimepiride-pioglitazone_category is highly imbalanced (> 99.9%) Imbalance
metformin-pioglitazone_category is highly imbalanced (> 99.9%) Imbalance
age_category is highly imbalanced (83.1%) Imbalance
num_procedures has 33994 (46.3%) zeros Zeros
number_inpatient has 52539 (71.5%) zeros Zeros

Reproduction

Analysis started2024-11-09 15:25:45.178925
Analysis finished2024-11-09 15:26:14.707784
Duration29.53 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3135875
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-11-09T15:26:14.899290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9347354
Coefficient of variation (CV)0.68034679
Kurtosis0.97834875
Mean4.3135875
Median Absolute Deviation (MAD)2
Skewness1.1644314
Sum316833
Variance8.6126719
MonotonicityNot monotonic
2024-11-09T15:26:15.688058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 12967
17.7%
2 12704
17.3%
1 10560
14.4%
4 10071
13.7%
5 7195
9.8%
6 5428
7.4%
7 4108
 
5.6%
8 3038
 
4.1%
9 2073
 
2.8%
10 1584
 
2.2%
Other values (4) 3722
 
5.1%
ValueCountFrequency (%)
1 10560
14.4%
2 12704
17.3%
3 12967
17.7%
4 10071
13.7%
5 7195
9.8%
6 5428
7.4%
7 4108
 
5.6%
8 3038
 
4.1%
9 2073
 
2.8%
10 1584
 
2.2%
ValueCountFrequency (%)
14 684
 
0.9%
13 802
 
1.1%
12 982
 
1.3%
11 1254
 
1.7%
10 1584
 
2.2%
9 2073
 
2.8%
8 3038
4.1%
7 4108
5.6%
6 5428
7.4%
5 7195
9.8%

num_lab_procedures
Real number (ℝ)

Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.801906
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-11-09T15:26:15.994698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q132
median44
Q356
95-th percentile72
Maximum93
Range92
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.1731
Coefficient of variation (CV)0.44794967
Kurtosis-0.26454269
Mean42.801906
Median Absolute Deviation (MAD)12
Skewness-0.29830769
Sum3143800
Variance367.60775
MonotonicityNot monotonic
2024-11-09T15:26:16.341977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2271
 
3.1%
43 2085
 
2.8%
44 1789
 
2.4%
45 1724
 
2.3%
46 1664
 
2.3%
38 1648
 
2.2%
40 1648
 
2.2%
47 1589
 
2.2%
42 1587
 
2.2%
41 1584
 
2.2%
Other values (83) 55861
76.1%
ValueCountFrequency (%)
1 2271
3.1%
2 828
 
1.1%
3 509
 
0.7%
4 284
 
0.4%
5 207
 
0.3%
6 158
 
0.2%
7 189
 
0.3%
8 265
 
0.4%
9 641
 
0.9%
10 636
 
0.9%
ValueCountFrequency (%)
93 39
 
0.1%
92 29
 
< 0.1%
91 43
0.1%
90 46
0.1%
89 44
0.1%
88 69
0.1%
87 56
0.1%
86 81
0.1%
85 92
0.1%
84 104
0.1%

num_procedures
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3102519
Minimum0
Maximum6
Zeros33994
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-11-09T15:26:16.592340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6737219
Coefficient of variation (CV)1.2774047
Kurtosis0.93773686
Mean1.3102519
Median Absolute Deviation (MAD)1
Skewness1.3295509
Sum96238
Variance2.8013449
MonotonicityNot monotonic
2024-11-09T15:26:16.846875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 33994
46.3%
1 14927
20.3%
2 9237
 
12.6%
3 6955
 
9.5%
6 3187
 
4.3%
4 2900
 
3.9%
5 2250
 
3.1%
ValueCountFrequency (%)
0 33994
46.3%
1 14927
20.3%
2 9237
 
12.6%
3 6955
 
9.5%
4 2900
 
3.9%
5 2250
 
3.1%
6 3187
 
4.3%
ValueCountFrequency (%)
6 3187
 
4.3%
5 2250
 
3.1%
4 2900
 
3.9%
3 6955
 
9.5%
2 9237
 
12.6%
1 14927
20.3%
0 33994
46.3%

num_medications
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.999823
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-11-09T15:26:17.125536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median14
Q319
95-th percentile28
Maximum35
Range34
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8075553
Coefficient of variation (CV)0.45384238
Kurtosis-0.063805099
Mean14.999823
Median Absolute Deviation (MAD)5
Skewness0.55596784
Sum1101737
Variance46.342809
MonotonicityNot monotonic
2024-11-09T15:26:17.432491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
12 4551
 
6.2%
13 4508
 
6.1%
11 4375
 
6.0%
14 4195
 
5.7%
15 4180
 
5.7%
10 4121
 
5.6%
16 3950
 
5.4%
9 3838
 
5.2%
17 3568
 
4.9%
8 3440
 
4.7%
Other values (25) 32724
44.6%
ValueCountFrequency (%)
1 210
 
0.3%
2 383
 
0.5%
3 750
 
1.0%
4 1158
 
1.6%
5 1663
2.3%
6 2148
2.9%
7 2816
3.8%
8 3440
4.7%
9 3838
5.2%
10 4121
5.6%
ValueCountFrequency (%)
35 285
 
0.4%
34 317
 
0.4%
33 369
 
0.5%
32 431
 
0.6%
31 498
0.7%
30 582
0.8%
29 691
0.9%
28 853
1.2%
27 1020
1.4%
26 1107
1.5%

number_outpatient
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73450
100.0%

Length

2024-11-09T15:26:17.731328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:17.947128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73450
100.0%

Most occurring characters

ValueCountFrequency (%)
0 73450
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

number_emergency
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73450
100.0%

Length

2024-11-09T15:26:18.183180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:18.415858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73450
100.0%

Most occurring characters

ValueCountFrequency (%)
0 73450
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

number_inpatient
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48014976
Minimum0
Maximum19
Zeros52539
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-11-09T15:26:18.622476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.99877771
Coefficient of variation (CV)2.0801379
Kurtosis19.729898
Mean0.48014976
Median Absolute Deviation (MAD)0
Skewness3.4683825
Sum35267
Variance0.99755692
MonotonicityNot monotonic
2024-11-09T15:26:18.886752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 52539
71.5%
1 13056
 
17.8%
2 4466
 
6.1%
3 1842
 
2.5%
4 815
 
1.1%
5 356
 
0.5%
6 176
 
0.2%
7 96
 
0.1%
8 39
 
0.1%
9 31
 
< 0.1%
Other values (7) 34
 
< 0.1%
ValueCountFrequency (%)
0 52539
71.5%
1 13056
 
17.8%
2 4466
 
6.1%
3 1842
 
2.5%
4 815
 
1.1%
5 356
 
0.5%
6 176
 
0.2%
7 96
 
0.1%
8 39
 
0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
14 3
 
< 0.1%
12 7
 
< 0.1%
11 7
 
< 0.1%
10 14
 
< 0.1%
9 31
 
< 0.1%
8 39
0.1%
7 96
0.1%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2837985
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-11-09T15:26:19.157959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.974132
Coefficient of variation (CV)0.27103056
Kurtosis-0.33387498
Mean7.2837985
Median Absolute Deviation (MAD)1
Skewness-0.78259584
Sum534995
Variance3.897197
MonotonicityNot monotonic
2024-11-09T15:26:19.442574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 33205
45.2%
5 9053
 
12.3%
6 7843
 
10.7%
8 7798
 
10.6%
7 7747
 
10.5%
4 4395
 
6.0%
3 2321
 
3.2%
2 835
 
1.1%
1 194
 
0.3%
16 24
 
< 0.1%
Other values (6) 35
 
< 0.1%
ValueCountFrequency (%)
1 194
 
0.3%
2 835
 
1.1%
3 2321
 
3.2%
4 4395
 
6.0%
5 9053
 
12.3%
6 7843
 
10.7%
7 7747
 
10.5%
8 7798
 
10.6%
9 33205
45.2%
10 8
 
< 0.1%
ValueCountFrequency (%)
16 24
 
< 0.1%
15 3
 
< 0.1%
14 4
 
< 0.1%
13 8
 
< 0.1%
12 6
 
< 0.1%
11 6
 
< 0.1%
10 8
 
< 0.1%
9 33205
45.2%
8 7798
 
10.6%
7 7747
 
10.5%

race_category
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
55585 
0
17865 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 55585
75.7%
0 17865
 
24.3%

Length

2024-11-09T15:26:19.729434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:19.965262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 55585
75.7%
0 17865
 
24.3%

Most occurring characters

ValueCountFrequency (%)
1 55585
75.7%
0 17865
 
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 55585
75.7%
0 17865
 
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 55585
75.7%
0 17865
 
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 55585
75.7%
0 17865
 
24.3%

gender_category
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
39219 
1
34231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 39219
53.4%
1 34231
46.6%

Length

2024-11-09T15:26:20.222513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:20.531871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 39219
53.4%
1 34231
46.6%

Most occurring characters

ValueCountFrequency (%)
0 39219
53.4%
1 34231
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39219
53.4%
1 34231
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39219
53.4%
1 34231
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39219
53.4%
1 34231
46.6%

metformin_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
59039 
1
13197 
3
 
808
2
 
406

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 59039
80.4%
1 13197
 
18.0%
3 808
 
1.1%
2 406
 
0.6%

Length

2024-11-09T15:26:21.140502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:21.555422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 59039
80.4%
1 13197
 
18.0%
3 808
 
1.1%
2 406
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 59039
80.4%
1 13197
 
18.0%
3 808
 
1.1%
2 406
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 59039
80.4%
1 13197
 
18.0%
3 808
 
1.1%
2 406
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 59039
80.4%
1 13197
 
18.0%
3 808
 
1.1%
2 406
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 59039
80.4%
1 13197
 
18.0%
3 808
 
1.1%
2 406
 
0.6%

repaglinide_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72396 
1
 
944
3
 
77
2
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 72396
98.6%
1 944
 
1.3%
3 77
 
0.1%
2 33
 
< 0.1%

Length

2024-11-09T15:26:22.119009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:22.671862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 72396
98.6%
1 944
 
1.3%
3 77
 
0.1%
2 33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72396
98.6%
1 944
 
1.3%
3 77
 
0.1%
2 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72396
98.6%
1 944
 
1.3%
3 77
 
0.1%
2 33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72396
98.6%
1 944
 
1.3%
3 77
 
0.1%
2 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72396
98.6%
1 944
 
1.3%
3 77
 
0.1%
2 33
 
< 0.1%

nateglinide_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72984 
1
 
438
3
 
18
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72984
99.4%
1 438
 
0.6%
3 18
 
< 0.1%
2 10
 
< 0.1%

Length

2024-11-09T15:26:23.176411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:23.582491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 72984
99.4%
1 438
 
0.6%
3 18
 
< 0.1%
2 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72984
99.4%
1 438
 
0.6%
3 18
 
< 0.1%
2 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72984
99.4%
1 438
 
0.6%
3 18
 
< 0.1%
2 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72984
99.4%
1 438
 
0.6%
3 18
 
< 0.1%
2 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72984
99.4%
1 438
 
0.6%
3 18
 
< 0.1%
2 10
 
< 0.1%

chlorpropamide_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73380 
1
 
64
3
 
5
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73380
99.9%
1 64
 
0.1%
3 5
 
< 0.1%
2 1
 
< 0.1%

Length

2024-11-09T15:26:23.842139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:24.094865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73380
99.9%
1 64
 
0.1%
3 5
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73380
99.9%
1 64
 
0.1%
3 5
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73380
99.9%
1 64
 
0.1%
3 5
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73380
99.9%
1 64
 
0.1%
3 5
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73380
99.9%
1 64
 
0.1%
3 5
 
< 0.1%
2 1
 
< 0.1%

glimepiride_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
69726 
1
 
3346
3
 
241
2
 
137

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69726
94.9%
1 3346
 
4.6%
3 241
 
0.3%
2 137
 
0.2%

Length

2024-11-09T15:26:24.352003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:24.602395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 69726
94.9%
1 3346
 
4.6%
3 241
 
0.3%
2 137
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 69726
94.9%
1 3346
 
4.6%
3 241
 
0.3%
2 137
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 69726
94.9%
1 3346
 
4.6%
3 241
 
0.3%
2 137
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 69726
94.9%
1 3346
 
4.6%
3 241
 
0.3%
2 137
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 69726
94.9%
1 3346
 
4.6%
3 241
 
0.3%
2 137
 
0.2%

acetohexamide_category
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73449 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Length

2024-11-09T15:26:24.860264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:25.094378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

glipizide_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
64421 
1
8096 
3
 
556
2
 
377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 64421
87.7%
1 8096
 
11.0%
3 556
 
0.8%
2 377
 
0.5%

Length

2024-11-09T15:26:25.329684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:25.631906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 64421
87.7%
1 8096
 
11.0%
3 556
 
0.8%
2 377
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 64421
87.7%
1 8096
 
11.0%
3 556
 
0.8%
2 377
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 64421
87.7%
1 8096
 
11.0%
3 556
 
0.8%
2 377
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 64421
87.7%
1 8096
 
11.0%
3 556
 
0.8%
2 377
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 64421
87.7%
1 8096
 
11.0%
3 556
 
0.8%
2 377
 
0.5%

glyburide_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
65584 
1
6856 
3
 
608
2
 
402

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 65584
89.3%
1 6856
 
9.3%
3 608
 
0.8%
2 402
 
0.5%

Length

2024-11-09T15:26:26.093577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:26.545493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 65584
89.3%
1 6856
 
9.3%
3 608
 
0.8%
2 402
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 65584
89.3%
1 6856
 
9.3%
3 608
 
0.8%
2 402
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 65584
89.3%
1 6856
 
9.3%
3 608
 
0.8%
2 402
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 65584
89.3%
1 6856
 
9.3%
3 608
 
0.8%
2 402
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 65584
89.3%
1 6856
 
9.3%
3 608
 
0.8%
2 402
 
0.5%

tolbutamide_category
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73431 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73431
> 99.9%
1 19
 
< 0.1%

Length

2024-11-09T15:26:27.064859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:27.445491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73431
> 99.9%
1 19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73431
> 99.9%
1 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73431
> 99.9%
1 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73431
> 99.9%
1 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73431
> 99.9%
1 19
 
< 0.1%

pioglitazone_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
68403 
1
 
4798
3
 
168
2
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68403
93.1%
1 4798
 
6.5%
3 168
 
0.2%
2 81
 
0.1%

Length

2024-11-09T15:26:27.900216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:28.240448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 68403
93.1%
1 4798
 
6.5%
3 168
 
0.2%
2 81
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 68403
93.1%
1 4798
 
6.5%
3 168
 
0.2%
2 81
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 68403
93.1%
1 4798
 
6.5%
3 168
 
0.2%
2 81
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 68403
93.1%
1 4798
 
6.5%
3 168
 
0.2%
2 81
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 68403
93.1%
1 4798
 
6.5%
3 168
 
0.2%
2 81
 
0.1%

rosiglitazone_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
68943 
1
 
4318
3
 
128
2
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 68943
93.9%
1 4318
 
5.9%
3 128
 
0.2%
2 61
 
0.1%

Length

2024-11-09T15:26:29.141190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:29.545872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 68943
93.9%
1 4318
 
5.9%
3 128
 
0.2%
2 61
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 68943
93.9%
1 4318
 
5.9%
3 128
 
0.2%
2 61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 68943
93.9%
1 4318
 
5.9%
3 128
 
0.2%
2 61
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 68943
93.9%
1 4318
 
5.9%
3 128
 
0.2%
2 61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 68943
93.9%
1 4318
 
5.9%
3 128
 
0.2%
2 61
 
0.1%

acarbose_category
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73259 
1
 
183
3
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73259
99.7%
1 183
 
0.2%
3 8
 
< 0.1%

Length

2024-11-09T15:26:30.041290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:30.515359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73259
99.7%
1 183
 
0.2%
3 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73259
99.7%
1 183
 
0.2%
3 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73259
99.7%
1 183
 
0.2%
3 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73259
99.7%
1 183
 
0.2%
3 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73259
99.7%
1 183
 
0.2%
3 8
 
< 0.1%

miglitol_category
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73428 
1
 
21
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73428
> 99.9%
1 21
 
< 0.1%
2 1
 
< 0.1%

Length

2024-11-09T15:26:30.967371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:31.212291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73428
> 99.9%
1 21
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73428
> 99.9%
1 21
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73428
> 99.9%
1 21
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73428
> 99.9%
1 21
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73428
> 99.9%
1 21
 
< 0.1%
2 1
 
< 0.1%

troglitazone_category
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73447 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73447
> 99.9%
1 3
 
< 0.1%

Length

2024-11-09T15:26:31.461507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:31.698064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73447
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73447
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73447
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73447
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73447
> 99.9%
1 3
 
< 0.1%

tolazamide_category
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73415 
1
 
34
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73415
> 99.9%
1 34
 
< 0.1%
3 1
 
< 0.1%

Length

2024-11-09T15:26:31.947456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:32.189143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73415
> 99.9%
1 34
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73415
> 99.9%
1 34
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73415
> 99.9%
1 34
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73415
> 99.9%
1 34
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73415
> 99.9%
1 34
 
< 0.1%
3 1
 
< 0.1%

examide_category
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73450
100.0%

Length

2024-11-09T15:26:32.434478image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:32.653564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73450
100.0%

Most occurring characters

ValueCountFrequency (%)
0 73450
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

citoglipton_category
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73450
100.0%

Length

2024-11-09T15:26:32.891180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:33.146492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73450
100.0%

Most occurring characters

ValueCountFrequency (%)
0 73450
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

insulin_category
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
35623 
1
22821 
2
8214 
3
6792 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 35623
48.5%
1 22821
31.1%
2 8214
 
11.2%
3 6792
 
9.2%

Length

2024-11-09T15:26:33.396843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:33.648699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 35623
48.5%
1 22821
31.1%
2 8214
 
11.2%
3 6792
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 35623
48.5%
1 22821
31.1%
2 8214
 
11.2%
3 6792
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35623
48.5%
1 22821
31.1%
2 8214
 
11.2%
3 6792
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35623
48.5%
1 22821
31.1%
2 8214
 
11.2%
3 6792
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35623
48.5%
1 22821
31.1%
2 8214
 
11.2%
3 6792
 
9.2%

glyburide-metformin_category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72949 
1
 
496
3
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72949
99.3%
1 496
 
0.7%
3 3
 
< 0.1%
2 2
 
< 0.1%

Length

2024-11-09T15:26:33.937278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:34.227864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 72949
99.3%
1 496
 
0.7%
3 3
 
< 0.1%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72949
99.3%
1 496
 
0.7%
3 3
 
< 0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72949
99.3%
1 496
 
0.7%
3 3
 
< 0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72949
99.3%
1 496
 
0.7%
3 3
 
< 0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72949
99.3%
1 496
 
0.7%
3 3
 
< 0.1%
2 2
 
< 0.1%

glipizide-metformin_category
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73444 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73444
> 99.9%
1 6
 
< 0.1%

Length

2024-11-09T15:26:34.487343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:34.721152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73444
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73444
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73444
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73444
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73444
> 99.9%
1 6
 
< 0.1%

glimepiride-pioglitazone_category
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73449 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Length

2024-11-09T15:26:34.962651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:35.209953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

metformin-rosiglitazone_category
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73450
100.0%

Length

2024-11-09T15:26:35.450005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:35.678496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73450
100.0%

Most occurring characters

ValueCountFrequency (%)
0 73450
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73450
100.0%

metformin-pioglitazone_category
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
73449 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Length

2024-11-09T15:26:35.903880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:36.150844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73449
> 99.9%
1 1
 
< 0.1%

age_mean
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.049013
Minimum5
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-11-09T15:26:36.352013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q155
median65
Q375
95-th percentile85
Maximum95
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.043988
Coefficient of variation (CV)0.24291033
Kurtosis0.39351506
Mean66.049013
Median Absolute Deviation (MAD)10
Skewness-0.66044636
Sum4851300
Variance257.40954
MonotonicityNot monotonic
2024-11-09T15:26:36.583829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
75 18935
25.8%
65 16122
21.9%
85 12423
16.9%
55 12378
16.9%
45 6943
 
9.5%
35 2668
 
3.6%
95 2145
 
2.9%
25 1093
 
1.5%
15 587
 
0.8%
5 156
 
0.2%
ValueCountFrequency (%)
5 156
 
0.2%
15 587
 
0.8%
25 1093
 
1.5%
35 2668
 
3.6%
45 6943
 
9.5%
55 12378
16.9%
65 16122
21.9%
75 18935
25.8%
85 12423
16.9%
95 2145
 
2.9%
ValueCountFrequency (%)
95 2145
 
2.9%
85 12423
16.9%
75 18935
25.8%
65 16122
21.9%
55 12378
16.9%
45 6943
 
9.5%
35 2668
 
3.6%
25 1093
 
1.5%
15 587
 
0.8%
5 156
 
0.2%

age_category
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
71614 
0
 
1836

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 71614
97.5%
0 1836
 
2.5%

Length

2024-11-09T15:26:36.842455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:37.078549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 71614
97.5%
0 1836
 
2.5%

Most occurring characters

ValueCountFrequency (%)
1 71614
97.5%
0 1836
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 71614
97.5%
0 1836
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 71614
97.5%
0 1836
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 71614
97.5%
0 1836
 
2.5%

change_category
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
41333 
1
32117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 41333
56.3%
1 32117
43.7%

Length

2024-11-09T15:26:37.339530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:37.573358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 41333
56.3%
1 32117
43.7%

Most occurring characters

ValueCountFrequency (%)
0 41333
56.3%
1 32117
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 41333
56.3%
1 32117
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 41333
56.3%
1 32117
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 41333
56.3%
1 32117
43.7%

diabetes-medication_category
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
55438 
0
18012 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 55438
75.5%
0 18012
 
24.5%

Length

2024-11-09T15:26:37.823399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:38.057187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 55438
75.5%
0 18012
 
24.5%

Most occurring characters

ValueCountFrequency (%)
1 55438
75.5%
0 18012
 
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 55438
75.5%
0 18012
 
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 55438
75.5%
0 18012
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 55438
75.5%
0 18012
 
24.5%

diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
42172 
1
31278 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 42172
57.4%
1 31278
42.6%

Length

2024-11-09T15:26:38.327090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:26:38.555596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 42172
57.4%
1 31278
42.6%

Most occurring characters

ValueCountFrequency (%)
0 42172
57.4%
1 31278
42.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 42172
57.4%
1 31278
42.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 42172
57.4%
1 31278
42.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 42172
57.4%
1 31278
42.6%

Interactions

2024-11-09T15:26:09.455217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:55.101813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:57.570091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:00.181553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:02.094214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:05.031159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:07.410882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:09.677665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:55.359640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:57.957589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:00.546057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:02.354390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:05.275061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:07.804654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:09.932712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:55.713440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:58.261412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:00.771573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:03.739535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:05.512087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:08.191090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:10.205703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:56.123877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:58.652731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:01.046154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:03.991940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:06.101352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:08.438359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:10.460266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:56.436416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:59.080343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:01.301955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:04.252722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:06.351239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:08.696620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:10.780149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:56.811024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:59.457046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:01.568296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:04.521484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:06.829586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:08.985755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:11.152802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:57.212715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:25:59.839300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:01.838404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:04.786211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:07.192449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:26:09.240301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-09T15:26:38.781767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
acarbose_categoryacetohexamide_categoryage_categoryage_meanchange_categorychlorpropamide_categorydiabetesdiabetes-medication_categorygender_categoryglimepiride-pioglitazone_categoryglimepiride_categoryglipizide-metformin_categoryglipizide_categoryglyburide-metformin_categoryglyburide_categoryinsulin_categorymetformin-pioglitazone_categorymetformin_categorymiglitol_categorynateglinide_categorynum_lab_proceduresnum_medicationsnum_proceduresnumber_diagnosesnumber_inpatientpioglitazone_categoryrace_categoryrepaglinide_categoryrosiglitazone_categorytime_in_hospitaltolazamide_categorytolbutamide_categorytroglitazone_category
acarbose_category1.0000.0000.0040.0040.0460.0000.0110.0290.0090.0000.0100.0000.0260.0090.0120.0020.0000.0170.0090.0000.0000.0120.0010.0000.0000.0090.0130.0160.0060.0050.0000.0000.000
acetohexamide_category0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0240.0160.0000.0000.0000.0000.0000.0000.0230.0000.0000.000
age_category0.0040.0001.0001.0000.0040.0000.0280.0240.0220.0000.0280.0000.0470.0120.0460.0850.0000.0400.0000.0100.0240.1680.0800.3000.0330.0350.0490.0150.0300.0650.0000.0000.000
age_mean0.0040.0001.0001.0000.0550.0050.0640.0510.1220.0000.0230.0000.0350.0090.0470.0680.0000.0640.0000.0050.0250.042-0.0520.2130.0450.0310.1890.0270.0250.1310.0000.0160.000
change_category0.0460.0000.0040.0551.0000.0130.0470.5020.0110.0000.1520.0040.2180.0440.2050.6280.0000.3490.0110.0550.0720.2430.0260.0550.0080.2070.0040.0870.2050.1010.0000.0000.004
chlorpropamide_category0.0000.0000.0000.0050.0131.0000.0030.0160.0000.0000.0000.0000.0000.0000.0000.0120.0000.0010.0000.0000.0000.0000.0000.0050.0000.0000.0080.0000.0020.0050.0000.0000.000
diabetes0.0110.0000.0280.0640.0470.0031.0000.0680.0130.0000.0090.0000.0260.0090.0040.0650.0000.0230.0020.0000.0400.0820.0370.1060.1640.0140.0180.0220.0140.0720.0030.0000.000
diabetes-medication_category0.0290.0000.0240.0510.5020.0160.0681.0000.0140.0000.1320.0000.2130.0470.1970.5870.0000.2820.0080.0450.0500.2090.0330.0360.0240.1550.0000.0680.1460.0610.0110.0070.000
gender_category0.0090.0000.0220.1220.0110.0000.0130.0141.0000.0000.0040.0040.0240.0050.0290.0000.0000.0000.0040.0000.0320.0460.0640.0090.0080.0090.0600.0000.0160.0470.0070.0000.003
glimepiride-pioglitazone_category0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0040.0050.0000.0000.0000.0000.0000.0000.0000.000
glimepiride_category0.0100.0000.0280.0230.1520.0000.0090.1320.0040.0001.0000.0000.0420.0030.0410.0120.0000.0290.0190.0120.0220.0310.0030.0100.0000.0270.0190.0040.0240.0230.0000.0000.006
glipizide-metformin_category0.0000.0000.0000.0000.0040.0000.0000.0000.0040.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0060.0000.0000.0000.0070.0000.0000.0000.0090.0000.0000.000
glipizide_category0.0260.0000.0470.0350.2180.0000.0260.2130.0240.0000.0420.0001.0000.0150.0630.0300.0000.0510.0000.0050.0230.0400.0060.0140.0020.0250.0160.0070.0290.0350.0000.0000.000
glyburide-metformin_category0.0090.0000.0120.0090.0440.0000.0090.0470.0050.0000.0030.0000.0151.0000.0040.0030.0000.0130.0000.0050.0080.0060.0000.0140.0000.0190.0000.0000.0000.0000.0000.0000.000
glyburide_category0.0120.0000.0460.0470.2050.0000.0040.1970.0290.0000.0410.0000.0630.0041.0000.0510.0000.0970.0030.0120.0220.0340.0070.0260.0110.0150.0220.0140.0260.0350.0000.0000.000
insulin_category0.0020.0000.0850.0680.6280.0120.0650.5870.0000.0000.0120.0000.0300.0030.0511.0000.0000.0260.0080.0050.0790.1360.0170.0810.0380.0120.0400.0230.0120.0710.0100.0000.000
metformin-pioglitazone_category0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0490.0000.0000.0060.0000.0000.0040.0000.0120.0000.0000.0000.0000.0000.0000.000
metformin_category0.0170.0000.0400.0640.3490.0010.0230.2820.0000.0000.0290.0000.0510.0130.0970.0260.0491.0000.0000.0160.0420.0440.0330.0480.0330.0330.0070.0050.0590.0270.0100.0050.000
miglitol_category0.0090.0000.0000.0000.0110.0000.0020.0080.0040.0000.0190.0000.0000.0000.0030.0080.0000.0001.0000.0010.0000.0000.0000.0000.0000.0000.0000.0060.0080.0000.0000.0000.000
nateglinide_category0.0000.0000.0100.0050.0550.0000.0000.0450.0000.0000.0120.0000.0050.0050.0120.0050.0000.0160.0011.0000.0080.0170.0020.0490.0000.0250.0140.0000.0100.0050.0000.0000.000
num_lab_procedures0.0000.0070.0240.0250.0720.0000.0400.0500.0320.0000.0220.0100.0230.0080.0220.0790.0060.0420.0000.0081.0000.223-0.0170.1460.0420.0200.0650.0190.0110.3220.0000.0050.000
num_medications0.0120.0240.1680.0420.2430.0000.0820.2090.0460.0040.0310.0060.0400.0060.0340.1360.0000.0440.0000.0170.2231.0000.3240.2950.0840.0470.0670.0220.0360.4320.0000.0030.002
num_procedures0.0010.0160.080-0.0520.0260.0000.0370.0330.0640.0000.0030.0000.0060.0000.0070.0170.0000.0330.0000.002-0.0170.3241.0000.065-0.0570.0090.0470.0000.0080.1460.0100.0000.000
number_diagnoses0.0000.0000.3000.2130.0550.0050.1060.0360.0090.0040.0100.0000.0140.0140.0260.0810.0040.0480.0000.0490.1460.2950.0651.0000.1120.0120.0990.0200.0070.2320.0080.0000.000
number_inpatient0.0000.0000.0330.0450.0080.0000.1640.0240.0080.0050.0000.0000.0020.0000.0110.0380.0000.0330.0000.0000.0420.084-0.0570.1121.0000.0140.0070.0000.0080.0990.0000.0000.000
pioglitazone_category0.0090.0000.0350.0310.2070.0000.0140.1550.0090.0000.0270.0070.0250.0190.0150.0120.0120.0330.0000.0250.0200.0470.0090.0120.0141.0000.0270.0180.0370.0230.0000.0000.000
race_category0.0130.0000.0490.1890.0040.0080.0180.0000.0600.0000.0190.0000.0160.0000.0220.0400.0000.0070.0000.0140.0650.0670.0470.0990.0070.0271.0000.0170.0150.0110.0010.0050.000
repaglinide_category0.0160.0000.0150.0270.0870.0000.0220.0680.0000.0000.0040.0000.0070.0000.0140.0230.0000.0050.0060.0000.0190.0220.0000.0200.0000.0180.0171.0000.0080.0270.0000.0000.000
rosiglitazone_category0.0060.0000.0300.0250.2050.0020.0140.1460.0160.0000.0240.0000.0290.0000.0260.0120.0000.0590.0080.0100.0110.0360.0080.0070.0080.0370.0150.0081.0000.0180.0000.0000.004
time_in_hospital0.0050.0230.0650.1310.1010.0050.0720.0610.0470.0000.0230.0090.0350.0000.0350.0710.0000.0270.0000.0050.3220.4320.1460.2320.0990.0230.0110.0270.0181.0000.0000.0000.015
tolazamide_category0.0000.0000.0000.0000.0000.0000.0030.0110.0070.0000.0000.0000.0000.0000.0000.0100.0000.0100.0000.0000.0000.0000.0100.0080.0000.0000.0010.0000.0000.0001.0000.0000.000
tolbutamide_category0.0000.0000.0000.0160.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0050.0030.0000.0000.0000.0000.0050.0000.0000.0000.0001.0000.000
troglitazone_category0.0000.0000.0000.0000.0040.0000.0000.0000.0030.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0040.0150.0000.0001.000

Missing values

2024-11-09T15:26:11.695804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-09T15:26:13.477396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

time_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesrace_categorygender_categorymetformin_categoryrepaglinide_categorynateglinide_categorychlorpropamide_categoryglimepiride_categoryacetohexamide_categoryglipizide_categoryglyburide_categorytolbutamide_categorypioglitazone_categoryrosiglitazone_categoryacarbose_categorymiglitol_categorytroglitazone_categorytolazamide_categoryexamide_categorycitoglipton_categoryinsulin_categoryglyburide-metformin_categoryglipizide-metformin_categoryglimepiride-pioglitazone_categorymetformin-rosiglitazone_categorymetformin-pioglitazone_categoryage_meanage_categorychange_categorydiabetes-medication_categorydiabetes
7680224901900191010000001001000000100000551110
1405821901500281000000000000000000000000851001
5765215211200090000000000000000000100000651010
7953674711400161100000001000000000000000651011
7784974123200091011000010000000000200000851110
7814735411900191100000000000000000300000751111
247671593800171000000000000000000000000451001
9897368322600391100000000000000000000000651001
484864600800091000000001000000000000000751010
4882045801200241100000000000000000200000451111
time_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesrace_categorygender_categorymetformin_categoryrepaglinide_categorynateglinide_categorychlorpropamide_categoryglimepiride_categoryacetohexamide_categoryglipizide_categoryglyburide_categorytolbutamide_categorypioglitazone_categoryrosiglitazone_categoryacarbose_categorymiglitol_categorytroglitazone_categorytolazamide_categoryexamide_categorycitoglipton_categoryinsulin_categoryglyburide-metformin_categoryglipizide-metformin_categoryglimepiride-pioglitazone_categorymetformin-rosiglitazone_categorymetformin-pioglitazone_categoryage_meanage_categorychange_categorydiabetes-medication_categorydiabetes
6966425501000091110000000000000000000000851010
4999124311700050010000001000000000300000651111
6351034601900191010000000000000000000000651011
734041380700091100000001000000000000000651010
5353953811600091000000020000000000100000851110
2484332721800671100000000000000000000000651001
5939287653400091100000000000000000100000551010
5445523521500171000001000000000000000000651010
559011111700051000000000000000000000000651000
9375864001300171110000000000000000100000651110

Duplicate rows

Most frequently occurring

time_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesrace_categorygender_categorymetformin_categoryrepaglinide_categorynateglinide_categorychlorpropamide_categoryglimepiride_categoryacetohexamide_categoryglipizide_categoryglyburide_categorytolbutamide_categorypioglitazone_categoryrosiglitazone_categoryacarbose_categorymiglitol_categorytroglitazone_categorytolazamide_categoryexamide_categorycitoglipton_categoryinsulin_categoryglyburide-metformin_categoryglipizide-metformin_categoryglimepiride-pioglitazone_categorymetformin-rosiglitazone_categorymetformin-pioglitazone_categoryage_meanage_categorychange_categorydiabetes-medication_categorydiabetes# duplicates
01105000611000000000000000000000006510002
111314000610000000000000000000000005510002
21205000910000000000000000000000004510002
312208000710000000000000000000000008510002
412308000810000000000000000000000008510002
5129314000910000000000000000001000006510112
613005000410000000000000000000000006510002
713007000910000000000000000000000007510012
813165000511000000000000000000000006510012
913209000900000000000000000000000005510002